Applying computer vision techniques to distinguish between spontaneous and posed smiles is an active research topic of affective computing. Although there have been many works published addressing this problem and a couple of excellent benchmark databases created, the existing state-of-the-art approaches do not exploit the action units defined within the Facial Action Coding System that has become a standard in facial expression analysis. In this work, we explore the possibilities of extracting discriminative features directly from the dynamics of facial action units to differentiate between genuine and posed smiles. We report the results of our experimental study which shows that the proposed features offer competitive performance to those based on facial landmark analysis and on textural descriptors extracted from spatial-temporal blocks. We make these features publicly available for the UvA-NEMO and BBC databases, which will allow other researchers to further improve the classification scores, while preserving the interpretation capabilities attributed to the use of facial action units. Moreover, we have developed a new technique for identifying the smile phases, which is robust against the noise and allows for continuous analysis of facial videos.

应用计算机视觉技术来区分自然笑容和摆姿势笑容是情感计算的活跃研究主题。尽管已经出版了许多有关此问题的著作，并且创建了两个出色的基准数据库，但是现有的最新方法并未利用面部表情编码系统中定义的动作单元，而面部表情编码系统已成为面部表情的标准分析。在这项工作中，我们探索了直接从面部动作单元的动力学中提取区分特征的可能性，以区分真实的笑容和摆出的笑容。我们报告了我们的实验研究结果，结果表明，所提出的功能与基于面部地标分析和从时空块提取的纹理描述符的功能相比具有竞争优势。我们将这些功能公开提供给UvA-NEMO和BBC数据库，这将使其他研究人员可以进一步提高分类评分，同时保留归因于面部动作单位的解释能力。此外，我们已经开发出一种用于识别笑脸阶段的新技术，该技术可抗噪，并且可以连续分析面部视频。